Developing sufficient evidence on the clinical utility of cancer genomic applications is complex. The rapid pace of innovation in genomics means that studies must be extremely efficient if the evidence is to remain timely and relevant. This can limit the potential endpoints to short-term outcomes, or require retrospective designs to enable sufficient time for events to accumulate. There is a general lack of evidence for clinical utility, but there is also a need to clarify the meaning of clinical utility. For example, the concept of personal utility, or the value of knowing the information, is clearly relevant for some decision makers and settings (e.g., direct-to-consumer marketing), but may not be relevant in a clinical context.60
The metrics for measuring personal utility are not well established.58,66
It is also essential to identify the relevant comparator for CER, and to present data to enable appropriate comparisons.
To resolve questions on the clinical utility of genomic applications, a more comprehensive approach is needed. Very few genomic applications have sufficient evidence for widespread recommendation and use in clinical care. Research is needed that considers more outcome measures, and in settings that are relevant to more real-world clinical decisions. All stakeholders have a role in facilitating the generation of evidence. For example, health systems are needed to provide data and facilitate pragmatic trials, providers are needed to use genomic tests in the context of evidence generation, and test developers are needed to make tests available for collaborative study. A clear approach to developing priorities for CER research is also needed to ensure that limited resources are used to resolve the most compelling questions. Such approaches should engage stakeholders to ensure the study of pressing topics in ‘real-world’ environments and should proof approaches for rapid evidence synthesis and quantitatively assess the value of prioritized research, considering the health and well-being of patients and the decision-making needs of other stakeholders.
Second, it may be necessary to reform the evidentiary framework
to define evidence standards for clinical utility.6
This task that will require a dialogue and interaction between evidence appraisers and end users to develop consensus and to define acceptable alternatives to the current hierarchies of evidence. That is, to recognize that a RCT is not desirable or feasible in every circumstance, and to decide when (not if)
to use an observational study design and the extent to which evidence of underlying biological mechanisms contribute to the evidentiary frame-work.67
Beyond study designs, an evidentiary framework needs to cogently articulate the minimal evidence necessary before clinical application is warranted, taking into consideration issues around the type of genomic application and its clinical context.
Third, strategies that are rapid, timely, and efficient
are needed given the fast pace of discovery in genomic-based approaches. Existing methods are limited,68
and innovative methods are needed to make CER successful and relevant to decision making.69,70
New strategies will involve transformation of the research infrastructure to “learning systems” that allow continual addition to the evidence base. This approach will achieve greater efficiency through efforts such as establishing bio-repositories or registries, linking electronic medical record data or administrative databases to genomic information and creating quality-assured clinical data repositories, or improving standardized coding schemes for genomic applications.
Finally, any reforms of the evidentiary framework should uphold rigorous standards on the statistical validity
of the research.71
Although some study designs have a risk of greater uncertainty, we can make strategic choices about when such increased uncertainty is acceptable. We should improve the integrity and conduct of all study designs by using guidelines such as those provided in Strengthening the Reporting of Observational Studies in Epidemiology (STROBE), CONsolidated Standards of Reporting Trials Statement (CONSORT), STrengthening the REporting of Genetic Associations (STREGA), and Genetic RIsk Prediction Studies (GRIPS). Also, we can describe how threats to validity are assessed in grading evidence, or require pre-registry of the analysis plan for observational studies, as is currently done for RCTs, to reduce biases (including selective outcome reporting) or errors, such as from multiple testing.